检索结果-内蒙古大学图书馆

SSRN 2024年

作者： Hu, Fuyuan Zhang, Jian Lyu, Fan Zhao, Linglan Liu, Chengyan Zhang, Tao Yu, Baoqing School of Electronics and Information Engineering Suzhou University of Science and Technology No.99 Xuefu Road Jiangsu Suzhou215000 China Jiangsu Industrial Intelligent Low Carbon Technology Engineering Center Jiangsu Suzhou215009 China Suzhou Key Laboratory of Intelligent Low Carbon Technology Application Jiangsu Suzhou215009 China New Laboratory of Pattemn Recognition Institute of Automation Chinese Academy of Sciences College of Intelligence and Computing No.95 Zhongguancun East Road Beijing100190 China Tencent Youtu Lab Shanghai201600 China Seventh People’s Hospital Shanghai University of Traditional Chinese Medicine No.358 Datong Road Pudong New District Shanghai200137 China

Few-shot class-incremental learning (FSCIL) aims to build machine learning models that can continually learn novel classes from a few training samples, without forgetting knowledge of old classes. The challenges of FSCIL lie in the limited data of novel classes, which not only lead to significant overfitting issues but also exacerbate the notorious catastrophic forgetting problems. As proved in early studies, building sample relationships is beneficial for learning from few-shot classes. In this paper, we advance the idea to the incremental scenario and propose a Sample-to-Class (S2C) graph learning method for FSCIL. Concretely, we propose a Sample-level Graph Network (SGN) that focuses on analyzing sample relationships within a single session. This network helps aggregate similar samples, ultimately leading to the extraction of more refined class-level features. Then, we present a Class-level Graph Network (CGN) that establishes connections across class-level features of both novel and old classes. This network plays a crucial role in linking the knowledge between different sessions and helps improve overall learning performance in the FSCIL scenario. Moreover, we design a multi-stage strategy for training the S2C model, which mitigates the training challenges posed by limited data in the incremental learning process. The multi-stage training strategy is designed to build an S2C graph from base to incremental sessions and improve the capacity via an extra pseudo-incremental stage. Extensive experiments on three popular benchmark datasets show that our method outperforms the baselines and sets new state-of-the-art results in FSCIL. Our codes will be available upon acceptance. © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Generalize Ultrasound Image Segmentation Via Instant And Plug & Play Style Transfer

Generalize Ultrasound Image Segmentation Via Instant And Plu...

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IEEE International Symposium on Biomedical Imaging

作者： Zhendong Liu Xiaoqiong Huang Xin Yang Rui Gao Rui Li Yuanji Zhang Yankai Huang Guangquan Zhou Yi Xiong Alejandro F Frangi Dong Ni School of Biomedical Engineering Health Science Center Shenzhen University China Medical UltraSound Image Computing (MUSIC) Lab Shenzhen University China Luohu People’s Hospital Shenzhen China School of Biological Sciences and Medical Engineering Southeast University China Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB) School of Computing University of Leeds Leeds UK Medical Imaging Research Center (MIRC) University Hospital Gasthuisberg KU Leuven Leuven Belgium

Deep segmentation models that generalize to images with unknown appearance are important for real-world medical image analysis. Retraining models leads to high latency and complex pipelines, which are impractical in clinical settings. The situation becomes more severe for ultrasound image analysis because of their large appearance shifts. In this paper, we propose a novel method for robust segmentation under unknown appearance shifts. Our contribution is three-fold. First, we advance a one-stage plug-and-play solution by embedding hierarchical style transfer units into a segmentation architecture. Our solution can remove appearance shifts and perform segmentation simultaneously. Second, we adopt Dynamic Instance Normalization to conduct precise and dynamic style transfer in a learnable manner, rather than previously fixed style normalization. Third, our solution is fast and lightweight for routine clinical adoption. Given 400×400 image input, our solution only needs an additional 0.2 ms and 1.92M FLOPs to handle appearance shifts compared to the baseline pipeline. Extensive experiments are conducted on a large dataset from three vendors demonstrate our proposed method enhances the robustness of deep segmentation models.

关键词： Image segmentation Analytical models Ultrasonic imaging Image analysis Biological system modeling Pipelines Robustness

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Single-cell omics: experimental workflow, data analyses and applications

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Science China(Life Sciences) 2025年第1期68卷 5-102页

作者： Fengying Sun Haoyan Li Dongqing Sun Shaliu Fu Lei Gu Xin Shao Qinqin Wang Xin Dong Bin Duan Feiyang Xing Jun Wu Minmin Xiao Fangqing Zhao Jing-Dong J.Han Qi Liu Xiaohui Fan Chen Li Chenfei Wang Tieliu Shi Department of Clinical Laboratory the Affiliated Wuhu Hospital of East China Normal University(The Second People’s Hospital of Wuhu City)Wuhu 241000China Pharmaceutical Informatics Institute College of Pharmaceutical SciencesZhejiang UniversityHangzhou 310058China Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration(Tongji University) Ministry of EducationOrthopaedic DepartmentTongji HospitalBioinformatics DepartmentSchool of Life Sciences and TechnologyTongji UniversityShanghai 200082China Frontier Science Center for Stem Cells School of Life Sciences and TechnologyTongji UniversityShanghai 200092China Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine Shanghai East HospitalBioinformatics DepartmentSchool of Life Sciences and TechnologyTongji UniversityShanghai 200082China Research Institute of Intelligent Computing Zhejiang LabHangzhou 311121China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai 201210China Center for Single-cell Omics School of Public HealthShanghai Jiao Tong University School of MedicineShanghai 200025China National Key Laboratory of Chinese Medicine Modernization Innovation Center of Yangtze River DeltaZhejiang UniversityJiaxing 314103China Center for Bioinformatics and Computational Biology Shanghai Key Laboratory of Regulatory Biologythe Institute of Biomedical Sciences and School of Life SciencesEast China Normal UniversityShanghai 200241China Beijing Institutes of Life Science Chinese Academy of SciencesBeijing 100101China Peking-Tsinghua Center for Life Sciences Academy for Advanced Interdisciplinary StudiesCenter for Quantitative Biology(CQB)Peking UniversityBeijing 100871China Zhejiang Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases Women’s HospitalZhejiang University School of MedicineHangzhou 310006China Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE School of StatisticsEast China Normal U

Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular *** exploration of how the genomes orchestrate the formation and maintenance of each cell,and control the cellular phenotypes of various organismsis,is both captivating and *** the inception of the first single-cell RNA technology,technologies related to single-cell sequencing have experienced rapid advancements in recent *** technologies have expanded horizontally to include single-cell genome,epigenome,proteome,and metabolome,while vertically,they have progressed to integrate multiple omics data and incorporate additional information such as spatial scRNA-seq and CRISPR ***-cell omics represent a groundbreaking advancement in the biomedical field,offering profound insights into the understanding of complex diseases,including ***,we comprehensively summarize recent advances in single-cell omics technologies,with a specific focus on the methodology *** overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.

关键词： single-cell sequencing genome epigenome proteomics metabolomics multimodal spatial transcriptomics CRISPR screening

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Generalize ultrasound image segmentation via instant and plug & play style transfer

arXiv

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arXiv 2021年

作者： Liu, Zhendong Huang, Xiaoqiong Yang, Xin Gao, Rui Li, Rui Zhang, Yuanji Huang, Yankai Zhou, Guangquan Xiong, Yi Frangi, Alejandro F. Ni, Dong School of Biomedical Engineering Health Science Center Shenzhen University China Lab Shenzhen University China Department of Ultrasound Luohu People’s Hospital Shenzhen China School of Biological Sciences and Medical Engineering Southeast University China School of Computing University of Leeds Leeds United Kingdom University Hospital Gasthuisberg Electrical Engineering Department KU Leuven Leuven Belgium

Deep segmentation models that generalize to images with unknown appearance are important for realworld medical image analysis. Retraining models leads to high latency and complex pipelines, which are impractical in clinical settings. The situation becomes more severe for ultrasound image analysis because of their large appearance shifts. In this paper, we propose a novel method for robust segmentation under unknown appearance shifts. Our contribution is threefold. First, we advance a onestage plugandplay solution by embedding hierarchical style transfer units into a segmentation architecture. Our solution can remove appearance shifts and perform segmentation simultaneously. Second, we adopt Dynamic Instance Normalization to conduct precise and dynamic style transfer in a learnable manner, rather than previously fixed style normalization. Third, our solution is fast and lightweight for routine clinical adoption. Given 400 × 400 image input, our solution only needs an additional 0.2 ms and 1.92M FLOPs to handle appearance shifts compared to the baseline pipeline. Extensive experiments are conducted on a large dataset from three vendors demonstrate our proposed method enhances the robustness of deep segmentation models. Copyright © 2021, The Authors. All rights reserved.

关键词： Ultrasonics

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Visible-Thermal Cross-Modality Class-Incremental Learning

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Expert Systems with Applications 2025年

作者： Xinjie Yao Yingxue Wang Yu Wang Pengfei Zhu Ruipu Zhao Weihao Li Shenglei Pei Wanyu Lin College of Intelligence and Computing Tianjin University Tianjin 300350 China Engineering Research Center of City Intelligence and Digital Governance Ministry of Education of the People’s Republic of China Tianjin 300350 China Haihe Lab of ITAI Tianjin 300350 China National Engineering Research Center Public Safety Risk Perception and Control by Big Data Beijing 100041 China School of New Media and Communication Tianjin University Tianjin 300350 China School of Intelligence Science and Engineering Qinghai Minzu University Xining 810007 China Department of Computing The Hong Kong Polytechnic University Hong Kong 999077 China

Visible-thermal cross-modality learning has gained significant success by utilizing information from different sources to improve performance in downstream tasks. To make cross-modality learning methods more practical in open-ended environments, such as autonomous driving, a straightforward question arises on how we can incrementally update the model with such cross-modality data. In response, we propose a practical task, namely cross-modality class-incremental learning (CIL). We first provide a cross-modality CIL benchmark, which encompasses datasets abundant in scene diversity and environmental variability, including urban, residential, and diverse temporal settings. Then we thoroughly analyze its challenges and find that: (1) simply using cross-modality learning or CIL methods cannot achieve satisfactory performance in cross-modality CIL; (2) combining existing cross-modality learning methods and CIL strategies benefits cross-modality CIL to a very limited extent; (3) modality forgetting, i.e., neurons favoring a dominant modality and diminishing the weaker, is the significant factor that leads to forgetting in cross-modality CIL. To address the challenges of cross-modality CIL, we design a simple yet effective method, termed Hybrid-fused Forgetting Prevention (HFP). Specifically, HFP first introduces a block-diagonal structured sparsity on the channel dimension and implements the information interchange mechanism by shuffling the feature maps of different groups and changing the sequence of channels, thus fully exploiting the complementarity of cross-modality data. Then, with representation fusion and forgetting prevention strategies, more complementary features can be learned and exploited in retaining old knowledge, which can alleviate modality forgetting. Extensive experiments validate that HFP contributes a favorable framework to cross-modality CIL and alleviates forgetting effectively.

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Agent with Tangent-based Formulation and Anatomical Perception for Standard Plane Localization in 3D Ultrasound

arXiv

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arXiv 2022年

作者： Zou, Yuxin Dou, Haoran Huang, Yuhao Yang, Xin Qian, Jikuan Zhen, Chaojiong Ji, Xiaodan Ravikumar, Nishant Chen, Guoqiang Huang, Weijun Frangi, Alejandro F. Ni, Dong National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Health Science Center Shenzhen University China Lab Shenzhen University China Marshall Laboratory of Biomedical Engineering Shenzhen University China School of Computing University of Leeds Leeds United Kingdom School of Medicine University of Leeds Leeds United Kingdom Shenzhen RayShape Medical Technology Co. Ltd China Department of Ultrasound The First People's Hospital of Foshan Foshan China Department of Cardiovascular Sciences and Electrical Engineering KU Leuven Leuven Belgium Alan Turing Institute London United Kingdom

Standard plane (SP) localization is essential in routine clinical ultrasound (US) diagnosis. Compared to 2D US, 3D US can acquire multiple view planes in one scan and provide complete anatomy with the addition of coronal plane. However, manually navigating SPs in 3D US is laborious and biased due to the orientation variability and huge search space. In this study, we introduce a novel reinforcement learning (RL) framework for automatic SP localization in 3D US. Our contribution is three-fold. First, we formulate SP localization in 3D US as a tangent-point-based problem in RL to restructure the action space and significantly reduce the search space. Second, we design an auxiliary task learning strategy to enhance the model's ability to recognize subtle differences crossing Non-SPs and SPs in plane search. Finally, we propose a spatial-anatomical reward to effectively guide learning trajectories by exploiting spatial and anatomical information simultaneously. We explore the efficacy of our approach on localizing four SPs on uterus and fetal brain datasets. The experiments indicate that our approach achieves a high localization accuracy as well as robust performance. Copyright © 2022, The Authors. All rights reserved.

关键词： Ultrasonics

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CTSpine1K: A Large-Scale Dataset for Spinal Vertebrae Segmentation in Computed Tomography

arXiv

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arXiv 2021年

作者： Deng, Yang Wang, Ce Hui, Yuan Li, Qian Li, Jun Luo, Shiwei Sun, Mengke Quan, Quan Yang, Shuxin Hao, You Liu, Pengbo Xiao, Honghu Zhao, Chunpeng Wu, Xinbao Zhou, S. Kevin Key Lab of Intelligent Information Processing of Chinese Academy of Sciences Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Suzhou Institute of Intelligent Computing Technology Chinese Academy of Sciences Suzhou215028 China School of Biomedical Engineering Division of Life Sciences and Medicine University of Science and Technology of China Anhui Hefei China Suzhou Institute for Advanced Research University of Science and Technology of China Jiangsu Suzhou China Department of Radiology Guangzhou First People’s Hospital School of Medicine South China University of Technology Guangzhou510006 China Beijing Jishuitan Hospital Beijing100035 China

Spine-related diseases have high morbidity and cause a huge burden of social cost. Spine imaging is an essential tool for noninvasively visualizing and assessing spinal pathology. Segmenting vertebrae in computed tomography (CT) images has always been the base of quantitative medical image analysis for clinical diagnosis and surgery planning of spine diseases. Current publicly available annotated datasets on spinal vertebrae are small in size. Due to the lack of a large-scale annotated spine image dataset, the mainstream deep learning-based segmentation methods, which are data-driven, are heavily restricted. In this paper, we introduce a large-scale spine CT dataset called CTSpine1K, curated from multiple sources for vertebra segmentation, which contains 1,005 CT volumes with over 500,000 labeled vertebrae slices and 11,172 vertebrae belonging to different spinal conditions. Based on this dataset, we conducted several spinal vertebrae segmentation experiments to set the first benchmark. We believe that this large-scale dataset will facilitate further research in many spine-related image analysis tasks, including but not limited to vertebrae segmentation, labeling, 3D spine reconstruction from biplanar radiographs, and image superresolution and enhancement. Our dataset are publically available at https://***/data/archive/projects/africai_miccai2024_ctspine1k and https://***/MIRACLE-Center/CTSpine1K. . © 2021, CC BY.

关键词： Computerized tomography

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Flip Learning: Weakly Supervised Erase to Segment Nodules in Breast Ultrasound

arXiv

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arXiv 2025年

作者： Huang, Yuhao Chang, Ao Dou, Haoran Tao, Xing Zhou, Xinrui Cao, Yan Huang, Ruobing Frangi, Alejandro F. Bao, Lingyun Yang, Xin Ni, Dong National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Shenzhen China Lab Shenzhen University Shenzhen China Marshall Laboratory of Biomedical Engineering Shenzhen University Shenzhen China School of Computing University of Leeds Leeds United Kingdom Shenzhen RayShape Medical Technology Co. Ltd Shenzhen China Division of Informatics Imaging and Data Science School of Health Sciences University of Manchester Manchester United Kingdom Department of Computer Science School of Engineering University of Manchester Manchester United Kingdom Department of Electrical Engineering Department of Cardiovascular Sciences KU Leuven Belgium Alan Turing Institute London United Kingdom NIHR Manchester Biomedical Research Centre Manchester Academic Health Science Centre Manchester United Kingdom Department of Ultrasound Affiliated Hangzhou First People’s Hospital School of Medicine Westlake University China School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing China

Accurate segmentation of nodules in both 2D breast ultrasound (BUS) and 3D automated breast ultrasound (ABUS) is crucial for clinical diagnosis and treatment planning. Therefore, developing an automated system for nodule segmentation can enhance user independence and expedite clinical analysis. Unlike fully-supervised learning, weakly-supervised segmentation (WSS) can streamline the laborious and intricate annotation process. However, current WSS methods face challenges in achieving precise nodule segmentation, as many of them depend on inaccurate activation maps or inefficient pseudo-mask generation algorithms. In this study, we introduce a novel multi-agent reinforcement learning-based WSS framework called Flip Learning, which relies solely on 2D/3D boxes for accurate segmentation. Specifically, multiple agents are employed to erase the target from the box to facilitate classification tag flipping, with the erased region serving as the predicted segmentation mask. The key contributions of this research are as follows: 1) Adoption of a superpixel/supervoxel-based approach to encode the standardized environment, capturing boundary priors and expediting the learning process. 2) Introduction of three meticulously designed rewards, comprising a classification score reward and two intensity distribution rewards, to steer the agents’ erasing process precisely, thereby avoiding both under- and over-segmentation. 3) Implementation of a progressive curriculum learning strategy to enable agents to interact with the environment in a progressively challenging manner, thereby enhancing learning efficiency. Extensively validated on the large in-house BUS and ABUS datasets, our Flip Learning method outperforms state-of-the-art WSS methods and foundation models, and achieves comparable performance as fully-supervised learning algorithms. Copyright © 2025, The Authors. All rights reserved.

关键词： Reinforcement learning

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Remove Appearance Shift for Ultrasound Image Segmentation via Fast and Universal Style Transfer

Remove Appearance Shift for Ultrasound Image Segmentation vi...

引用

IEEE International Symposium on Biomedical Imaging

作者： Zhendong Liu Xin Yang Rui Gao Shengfeng Liu Haoran Dou Shuangchi He Yuhao Huang Yankai Huang Huanjia Luo Yuanji Zhang Yi Xiong Dong Ni National-Regional Key Technology Engineering Laboratory for Medical Ultrasound Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging School of Biomedical Engineering Health Science Center Shenzhen University Shenzhen China Medical UltraSound Image Computing (MUSIC) Lab Shenzhen University Shenzhen China Luohu People's Hosptial Shenzhen China

ISBN: (数字)9781538693308

ISBN: (纸本)9781538693315

Deep Neural Networks (DNNs) suffer from the performance degradation when image appearance shift occurs, especially in ultrasound (US) image segmentation. In this paper, we propose a novel and intuitive framework to remove the appearance shift, and hence improve the generalization ability of DNNs. Our work has three highlights. First, we follow the spirit of universal style transfer to remove appearance shifts, which was not explored before for US images. Without sacrificing image structure details, it enables the arbitrary style-content transfer. Second, accelerated with Adaptive Instance Normalization block, our framework achieved real-time speed required in the clinical US scanning. Third, an efficient and effective style image selection strategy is proposed to ensure the target-style US image and testing content US image properly match each other. Experiments on two large US datasets demonstrate that our methods are superior to state-of-the-art methods on making DNNs robust against various appearance shifts.

关键词： Image segmentation Testing Ultrasonic imaging Libraries Biomedical imaging Transforms

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Author Correction: Development and clinical deployment of a smartphone-based visual field deep learning system for glaucoma detection

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NPJ digital medicine 2022年第1期5卷 38页

作者： Fei Li Diping Song Han Chen Jian Xiong Xingyi Li Hua Zhong Guangxian Tang Sujie Fan Dennis S C Lam Weihua Pan Yajuan Zheng Ying Li Guoxiang Qu Junjun He Zhe Wang Ling Jin Rouxi Zhou Yunhe Song Yi Sun Weijing Cheng Chunman Yang Yazhi Fan Yingjie Li Hengli Zhang Ye Yuan Yang Xu Yunfan Xiong Lingfei Jin Aiguo Lv Lingzhi Niu Yuhong Liu Shaoli Li Jiani Zhang Linda M Zangwill Alejandro F Frangi Tin Aung Ching-Yu Cheng Yu Qiao Xiulan Zhang Daniel S W Ting State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-Sen University Guangzhou People's Republic of China. ShenZhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology The Chinese Academy of Sciences Shenzhen People's Republic of China. University of Chinese Academy of Sciences Beijing People's Republic of China. Department of Ophthalmology The First Affiliated Hospital of Kunming Medical University Kunming People's Republic of China. The First Hospital of Shijiazhuang City Shijiazhuang People's Republic of China. gxtykyy@***. Handan City Eye Hospital Handan People's Republic of China. C-MER (Shenzhen) Dennis Lam Eye Hospital International Eye Research Institute of The Chinese University of Hong Kong (Shenzhen) Shenzhen People's Republic of China. The Eye Hospital WMU at Hangzhou Hangzhou People's Republic of China. Department of Ophthalmology The Second Hospital of Jilin University Changchun People's Republic of China. SenseTime Group Limited Hong Kong People's Republic of China. Department of Ophthalmology The Second Affiliated Hospital of Guizhou Medical University Kaili People's Republic of China. Department of Ophthalmology The Second Affiliated Hospital of Xi'an Jiaotong University Xi'an People's Republic of China. Department of Ophthalmology The Third Affiliated Hospital of Nanchang University Nanchang People's Republic of China. The First Hospital of Shijiazhuang City Shijiazhuang People's Republic of China. Hamilton Glaucoma Center Shiley Eye Institute Viterbi Family Department of Ophthalmology UC San Diego La Jolla CA United States. CISTIB Center for Computational Imaging and Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds UK. Singapore Eye Research Institute and Singapore National Eye Centre Singapore Singapore. ShenZhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology The Chinese Academy of Sci

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